Deep learning based domain knowledge integration for small datasets: Illustrative applications in materials informatics

Zijiang Yang, Reda Al-Bahrani, Andrew C.E. Reid, Stefanos Papanikolaou, Surya R. Kalidindi, Wei Keng Liao, Alok Choudhary, Ankit Agrawal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Deep learning has shown its superiority to traditional machine learning methods in various fields, and in general, its success depends on the availability of large amounts of reliable data. However, in some scientific fields such as materials science, such big data is often expensive or even impossible to collect. Thus given relatively small datasets, most of data-driven methods are based on traditional machine learning methods, and it is challenging to apply deep learning for many tasks in these fields. In order to take the advantage of deep learning even for small datasets, a domain knowledge integration approach is proposed in this work. The efficacy of the proposed approach is tested on two materials science datasets with different types of inputs and outputs, for which domain knowledge-aware convolutional neural networks (CNNs) are developed and evaluated against traditional machine learning methods and standard CNN-based approaches. Experiment results demonstrate that integrating domain knowledge into deep learning can not only improve the model's performance for small datasets, but also make the prediction results more explainable based on domain knowledge.

Original languageEnglish (US)
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
StatePublished - Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: Jul 14 2019Jul 19 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period7/14/197/19/19

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Yang, Z., Al-Bahrani, R., Reid, A. C. E., Papanikolaou, S., Kalidindi, S. R., Liao, W. K., Choudhary, A., & Agrawal, A. (2019). Deep learning based domain knowledge integration for small datasets: Illustrative applications in materials informatics. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 [8852162] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852162